<p>Storms in the SE-US often evolve in different environments than those in the central Plains. Many poorly understood aspects of these differing environments may impact the tornadic potential of SE-US storms. Among these differences are potential variations in the CCN concentration owing to differences in land cover, combustion, industrial and urban activity, and proximity to maritime environments. The relative influence of warm and cold rain processes is sensitive to CCN concentration, with higher CCN concentrations producing smaller cloud droplets and more efficient cold rain processes. Cold rain processes result in DSDs with relatively larger drops from melting ice compared to warm rain processes. Differences in DSDs impact cold pool and downdraft size and strength, that influence tornado potential. This study investigates the impact of CCN concentration on DSDs in the SE-US by comparing DSDs from ARPS-EnKF model analyses and forecasts to observed DSDs from portable disdrometer-equipped probes collected by a collaboration between Purdue University, the University of Oklahoma (OU), the National Severe Storms Laboratory (NSSL), and the University of Massachusetts in a tornadic QLCS on 30 April 2017 during VORTEX-SE.</p><p>The ARPS-EnKF configuration, which consists of 40 ensemble members, is used with the NSSL triple-moment microphysics scheme. Surface and radar observations are both assimilated. Data assimilation experiments with CCN concentrations ranging from 100 cm^-3 (maritime) to 2,000 cm^-3 (continental) are conducted to characterize the variability of DSDs and the model output DSDs are verified against the disdrometer observations. The sensitivity of the DSD variability to CCN concentrations is evaluated. Results indicate continental CCN concentrations (close to CCN 1,000 cm³) produce DSDs that align closest to the observed DSDs. Other thermodynamic variables also accord better to observations in intermediate CCN concentration environments.</p>

A key element of hydrologic routing models is the storage-discharge relationship assumed to follow a certain mathematical form, usually a linear or a power function, the parameters of which are calibrated based on existing inflow-outflow data. While this assumption simplifies the model calibration process, it also constrains the models to operate by this function throughout their flow range. In view of the complex and nonlinear river flow behavior, this approximation undoubtedly introduces errors. This research presents a new hydrologic river routing approach that is not limited by the above assumption. River reaches are modeled as cascades of interacting conceptual reservoirs, with storage-discharge functions identified by the data. A novel parameter estimation approach has been developed to identify these functions and all other model parameters based on control theory concepts. After calibration, these functions indeed exhibit different mathematical forms at different regions of their active variation range. The new approach is applied and successfully demonstrated in real world reservoir and river routing applications from the Nile River Basin. A Bayesian forecasting scheme was also developed that uses the new approach to generate flow forecasts with explicit uncertainty characterization.

Modelos de distribuição de espécies, também conhecidos como modelos bioclimáticos ou modelos de nicho ecológico, têm sido aplicados em inúmeras questões ecológicas, incluindo espécies invasoras. Essa tese identificou as principais tendências e lacunas de estudos sobre o uso de modelos de distribuição de espécies para predizer a distribuição potencial de espécies invasoras (primeiro artigo). Adicionalmente, aplicou-se modelos de distribuição de espécies para predizer a distribuição potencial de Eragrostis plana Nees na América do Sul e verificar se ocorreu mudança de seu nicho bioclimático durante o processo de invasão (segundo artigo). Finalmente, avaliou-se a resposta em relação às áreas de ocorrência de cinco gramíneas Africanas invasoras nas Américas frente às mudanças climáticas (terceiro artigo). O primeiro artigo realiza uma análise cienciométrica sobre o uso de modelos de distribuição de espécies para predizer a distribuição potencial de espécies invasoras. O segundo artigo utiliza o Algoritmo GARP como técnica de modelagem e foram criados dois modelos para predizer a distribuição potencial de E. plana: um utilizando dados da região nativa da espécie (África do Sul) e outro com dados da região nativa e invadida (regiões da Argentina, Brasil e Uruguai). Posteriormente, cada modelo foi projetado na América do Sul para identificar regiões favoráveis ao estabelecimento de E. plana e verificar se os registros de ocorrência da espécie encontram-se dentro das regiões preditas com alta probabilidade pelos modelos. Além disso, a hipótese de que espécies podem alterar o seu nicho climático durante o processo de invasão foi avaliada para E. plana através de análises estatísticas multivariadas. O terceiro artigo aplica distintas técnicas de modelagem disponíveis no ambiente computacional BIOMOD, seguidas de conjunto de previsões para predizer a distribuição potencial das cinco gramíneas invasoras Africanas nas Américas frente às mudanças climáticas globais. / Species distribution models, also known as bioclimatic models or ecological niche models, have been applied in numerous ecological issues, including invasive species. This thesis indentified the main trends and gaps in studies on the use of species distribution models to predict the potential distribution of invasive species (first paper). Additionally, it includes species distribution modelling to predict the potential distribution of Eragrostis plana Nees in South America and assess the potential shift of its bioclimatic niche in the process of invasion (second paper). Finally, it includes as assessment of the response in terms of areas of occurrence of five invasive African grasses in the Americas under climate changes (third paper). The first paper provides a scientometric analysis on the use of species-distribution models to predict the potential distribution of invasive species. The second paper uses the algorithm GARP as modelling method and created two models to predict the potential distribution of E. plana: the first one used data from the native region (South Africa) and the second one data from both the native and invaded (Argentina, Brazil, and Uruguay). Subsequently, each model was projected in South America to identify regions favorable to the establishment of E. plana and assess whether the occurrence records are found within regions predicted by the models with high probability. Moreover, the hypothesis that species can shift their bioclimatic niche during the invasion process was evaluated for E. plana using multivariate statistical analysis. The third paper applies distinct modelling methods available in the BIOMOD package, followed by ensembles forecasting to predict the potential distribution of five invasive African grasses in Americas under climate changes.

Modelos de distribuição de espécies, também conhecidos como modelos bioclimáticos ou modelos de nicho ecológico, têm sido aplicados em inúmeras questões ecológicas, incluindo espécies invasoras. Essa tese identificou as principais tendências e lacunas de estudos sobre o uso de modelos de distribuição de espécies para predizer a distribuição potencial de espécies invasoras (primeiro artigo). Adicionalmente, aplicou-se modelos de distribuição de espécies para predizer a distribuição potencial de Eragrostis plana Nees na América do Sul e verificar se ocorreu mudança de seu nicho bioclimático durante o processo de invasão (segundo artigo). Finalmente, avaliou-se a resposta em relação às áreas de ocorrência de cinco gramíneas Africanas invasoras nas Américas frente às mudanças climáticas (terceiro artigo). O primeiro artigo realiza uma análise cienciométrica sobre o uso de modelos de distribuição de espécies para predizer a distribuição potencial de espécies invasoras. O segundo artigo utiliza o Algoritmo GARP como técnica de modelagem e foram criados dois modelos para predizer a distribuição potencial de E. plana: um utilizando dados da região nativa da espécie (África do Sul) e outro com dados da região nativa e invadida (regiões da Argentina, Brasil e Uruguai). Posteriormente, cada modelo foi projetado na América do Sul para identificar regiões favoráveis ao estabelecimento de E. plana e verificar se os registros de ocorrência da espécie encontram-se dentro das regiões preditas com alta probabilidade pelos modelos. Além disso, a hipótese de que espécies podem alterar o seu nicho climático durante o processo de invasão foi avaliada para E. plana através de análises estatísticas multivariadas. O terceiro artigo aplica distintas técnicas de modelagem disponíveis no ambiente computacional BIOMOD, seguidas de conjunto de previsões para predizer a distribuição potencial das cinco gramíneas invasoras Africanas nas Américas frente às mudanças climáticas globais. / Species distribution models, also known as bioclimatic models or ecological niche models, have been applied in numerous ecological issues, including invasive species. This thesis indentified the main trends and gaps in studies on the use of species distribution models to predict the potential distribution of invasive species (first paper). Additionally, it includes species distribution modelling to predict the potential distribution of Eragrostis plana Nees in South America and assess the potential shift of its bioclimatic niche in the process of invasion (second paper). Finally, it includes as assessment of the response in terms of areas of occurrence of five invasive African grasses in the Americas under climate changes (third paper). The first paper provides a scientometric analysis on the use of species-distribution models to predict the potential distribution of invasive species. The second paper uses the algorithm GARP as modelling method and created two models to predict the potential distribution of E. plana: the first one used data from the native region (South Africa) and the second one data from both the native and invaded (Argentina, Brazil, and Uruguay). Subsequently, each model was projected in South America to identify regions favorable to the establishment of E. plana and assess whether the occurrence records are found within regions predicted by the models with high probability. Moreover, the hypothesis that species can shift their bioclimatic niche during the invasion process was evaluated for E. plana using multivariate statistical analysis. The third paper applies distinct modelling methods available in the BIOMOD package, followed by ensembles forecasting to predict the potential distribution of five invasive African grasses in Americas under climate changes.

Modelos de distribuição de espécies, também conhecidos como modelos bioclimáticos ou modelos de nicho ecológico, têm sido aplicados em inúmeras questões ecológicas, incluindo espécies invasoras. Essa tese identificou as principais tendências e lacunas de estudos sobre o uso de modelos de distribuição de espécies para predizer a distribuição potencial de espécies invasoras (primeiro artigo). Adicionalmente, aplicou-se modelos de distribuição de espécies para predizer a distribuição potencial de Eragrostis plana Nees na América do Sul e verificar se ocorreu mudança de seu nicho bioclimático durante o processo de invasão (segundo artigo). Finalmente, avaliou-se a resposta em relação às áreas de ocorrência de cinco gramíneas Africanas invasoras nas Américas frente às mudanças climáticas (terceiro artigo). O primeiro artigo realiza uma análise cienciométrica sobre o uso de modelos de distribuição de espécies para predizer a distribuição potencial de espécies invasoras. O segundo artigo utiliza o Algoritmo GARP como técnica de modelagem e foram criados dois modelos para predizer a distribuição potencial de E. plana: um utilizando dados da região nativa da espécie (África do Sul) e outro com dados da região nativa e invadida (regiões da Argentina, Brasil e Uruguai). Posteriormente, cada modelo foi projetado na América do Sul para identificar regiões favoráveis ao estabelecimento de E. plana e verificar se os registros de ocorrência da espécie encontram-se dentro das regiões preditas com alta probabilidade pelos modelos. Além disso, a hipótese de que espécies podem alterar o seu nicho climático durante o processo de invasão foi avaliada para E. plana através de análises estatísticas multivariadas. O terceiro artigo aplica distintas técnicas de modelagem disponíveis no ambiente computacional BIOMOD, seguidas de conjunto de previsões para predizer a distribuição potencial das cinco gramíneas invasoras Africanas nas Américas frente às mudanças climáticas globais. / Species distribution models, also known as bioclimatic models or ecological niche models, have been applied in numerous ecological issues, including invasive species. This thesis indentified the main trends and gaps in studies on the use of species distribution models to predict the potential distribution of invasive species (first paper). Additionally, it includes species distribution modelling to predict the potential distribution of Eragrostis plana Nees in South America and assess the potential shift of its bioclimatic niche in the process of invasion (second paper). Finally, it includes as assessment of the response in terms of areas of occurrence of five invasive African grasses in the Americas under climate changes (third paper). The first paper provides a scientometric analysis on the use of species-distribution models to predict the potential distribution of invasive species. The second paper uses the algorithm GARP as modelling method and created two models to predict the potential distribution of E. plana: the first one used data from the native region (South Africa) and the second one data from both the native and invaded (Argentina, Brazil, and Uruguay). Subsequently, each model was projected in South America to identify regions favorable to the establishment of E. plana and assess whether the occurrence records are found within regions predicted by the models with high probability. Moreover, the hypothesis that species can shift their bioclimatic niche during the invasion process was evaluated for E. plana using multivariate statistical analysis. The third paper applies distinct modelling methods available in the BIOMOD package, followed by ensembles forecasting to predict the potential distribution of five invasive African grasses in Americas under climate changes.

Notre principal objectif est d'améliorer la qualité des prévisions de production d'énergie photovoltaïque (PV). Ces prévisions sont imparfaites à cause des incertitudes météorologiques et de l'imprécision des modèles statistiques convertissant les prévisions météorologiques en prévisions de production d'énergie. Grâce à une ou plusieurs prévisions météorologiques, nous générons de multiples prévisions de production PV et nous construisons une combinaison linéaire de ces prévisions de production. La minimisation du Continuous Ranked Probability Score (CRPS) permet de calibrer statistiquement la combinaison de ces prévisions, et délivre une prévision probabiliste sous la forme d'une fonction de répartition empirique pondérée.Dans ce contexte, nous proposons une étude du biais du CRPS et une étude des propriétés des scores propres pouvant se décomposer en somme de scores pondérés par seuil ou en somme de scores pondérés par quantile. Des techniques d'apprentissage séquentiel sont mises en oeuvre pour réaliser cette minimisation. Ces techniques fournissent des garanties théoriques de robustesse en termes de qualité de prévision, sous des hypothèses minimes. Ces méthodes sont appliquées à la prévision d'ensoleillement et à la prévision de production PV, fondée sur des prévisions météorologiques à haute résolution et sur des ensembles de prévisions classiques. / Our main objective is to improve the quality of photovoltaic power forecasts deriving from weather forecasts. Such forecasts are imperfect due to meteorological uncertainties and statistical modeling inaccuracies in the conversion of weather forecasts to power forecasts. First we gather several weather forecasts, secondly we generate multiple photovoltaic power forecasts, and finally we build linear combinations of the power forecasts. The minimization of the Continuous Ranked Probability Score (CRPS) allows to statistically calibrate the combination of these forecasts, and provides probabilistic forecasts under the form of a weighted empirical distribution function. We investigate the CRPS bias in this context and several properties of scoring rules which can be seen as a sum of quantile-weighted losses or a sum of threshold-weighted losses. The minimization procedure is achieved with online learning techniques. Such techniques come with theoretical guarantees of robustness on the predictive power of the combination of the forecasts. Essentially no assumptions are needed for the theoretical guarantees to hold. The proposed methods are applied to the forecast of solar radiation using satellite data, and the forecast of photovoltaic power based on high-resolution weather forecasts and standard ensembles of forecasts.

En prévision numérique du temps, les modèles de prévision d'ensemble sont devenus un outil incontournable pour quantifier l'incertitude des prévisions et fournir des prévisions probabilistes. Malheureusement, ces modèles ne sont pas parfaits et une correction simultanée de leur biais et de leur dispersion est nécessaire.Cette thèse présente de nouvelles méthodes de post-traitement statistique des prévisions d'ensemble. Celles-ci ont pour particularité d'être basées sur les forêts aléatoires.Contrairement à la plupart des techniques usuelles, ces méthodes non-paramétriques permettent de prendre en compte la dynamique non-linéaire de l'atmosphère.Elles permettent aussi d'ajouter des covariables (autres variables météorologiques, variables temporelles, géographiques...) facilement et sélectionnent elles-mêmes les prédicteurs les plus utiles dans la régression. De plus, nous ne faisons aucune hypothèse sur la distribution de la variable à traiter. Cette nouvelle approche surpasse les méthodes existantes pour des variables telles que la température et la vitesse du vent.Pour des variables reconnues comme difficiles à calibrer, telles que les précipitations sexti-horaires, des versions hybrides de nos techniques ont été créées. Nous montrons que ces versions hybrides (ainsi que nos versions originales) sont meilleures que les méthodes existantes. Elles amènent notamment une véritable valeur ajoutée pour les pluies extrêmes.La dernière partie de cette thèse concerne l'évaluation des prévisions d'ensemble pour les événements extrêmes. Nous avons montré quelques propriétés concernant le Continuous Ranked Probability Score (CRPS) pour les valeurs extrêmes. Nous avons aussi défini une nouvelle mesure combinant le CRPS et la théorie des valeurs extrêmes, dont nous examinons la cohérence sur une simulation ainsi que dans un cadre opérationnel.Les résultats de ce travail sont destinés à être insérés au sein de la chaîne de prévision et de vérification à Météo-France. / In numerical weather prediction, ensemble forecasts systems have become an essential tool to quantifyforecast uncertainty and to provide probabilistic forecasts. Unfortunately, these models are not perfect and a simultaneouscorrection of their bias and their dispersion is needed.This thesis presents new statistical post-processing methods for ensemble forecasting. These are based onrandom forests algorithms, which are non-parametric.Contrary to state of the art procedures, random forests can take into account non-linear features of atmospheric states. They easily allowthe addition of covariables (such as other weather variables, seasonal or geographic predictors) by a self-selection of the mostuseful predictors for the regression. Moreover, we do not make assumptions on the distribution of the variable of interest. This new approachoutperforms the existing methods for variables such as surface temperature and wind speed.For variables well-known to be tricky to calibrate, such as six-hours accumulated rainfall, hybrid versions of our techniqueshave been created. We show that these versions (and our original methods) are better than existing ones. Especially, they provideadded value for extreme precipitations.The last part of this thesis deals with the verification of ensemble forecasts for extreme events. We have shown several properties ofthe Continuous Ranked Probability Score (CRPS) for extreme values. We have also defined a new index combining the CRPS and the extremevalue theory, whose consistency is investigated on both simulations and real cases.The contributions of this work are intended to be inserted into the forecasting and verification chain at Météo-France.

Exploring the concepts of long range aerial transport of microorganisms is the main motivation of this study. For this purpose we use theories and concepts of dynamical systems in the context of geophysical fluid systems. We apply powerful notions such as finite-time Lyapunov exponent (FTLE) and the associated Lagrangian coherent structures (LCS) and we attempt to provide mathematical explanations and frameworks for some applied questions which are based on realistic concerns of atmospheric transport phenomena. Accordingly, we quantify the accuracy of prediction of FTLE-LCS features and we determine the sensitivity of such predictions to forecasting parameters. In addition, we consider the spatiotemporal resolution of the operational data sets and we propose the concept of probabilistic source and destination regions which leads to the definition of stochastic FTLE fields. Moreover, we put forward the idea of using ensemble forecasting to quantify the uncertainty of the forecast results. Finally, we investigate the statistical properties of localized measurements of atmospheric microbial structure and their connections to the concept of local FTLE time-series. Results of this study would pave the way for more efficient models and management strategies for the spread of infectious diseases affecting plants, domestic animals, and humans. / Ph. D.

This work aims to explore practical one-step-ahead forecasting of structurally changing data, an unstable behaviour, that real-life data connected to human activity often exhibit. This setting can be characterized as monitoring process. Various forecast models, methods and approaches can range from being simple and computationally "cheap" to very sophisticated and computationally "expensive". Moreover, different forecast methods handle different data-patterns and structural changes differently: for some particular data types or data intervals some particular forecast methods are better than the others, something that is usually not known beforehand. This raises a question: "Can one design a forecast procedure, that effectively and optimally switches between various forecast methods, adapting the forecast methods usage to the changes in the incoming data flow?" The thesis answers this question by introducing optimality concept, that allows optimal switching between simultaneously executed forecast methods, thus "tailoring" forecast methods to the changes in the data. It is also shown, how another forecast approach: combinational forecasting, where forecast methods are combined using weighted average, can be utilized by optimality principle and can therefore benefit from it. Thus, four classes of forecast results can be considered and compared: basic forecast methods, basic optimality, combinational forecasting, and combinational optimality. The thesis shows, that the usage of optimality gives results, where most of the time optimality is no worse or better than the best of forecast methods, that optimality is based on. Optimality reduces also scattering from multitude of various forecast suggestions to a single number or only a few numbers (in a controllable fashion). Optimality gives additionally lower bound for optimal forecasting: the hypothetically best achievable forecast result. The main conclusion is that optimality approach makes more or less obsolete other traditional ways of treating the monitoring processes: trying to find the single best forecast method for some structurally changing data. This search still can be sought, of course, but it is best done within optimality approach as its innate component. All this makes the proposed optimality approach for forecasting purposes a valid "representative" of a more broad ensemble approach (which likewise motivated development of now popular Ensemble Learning concept as a valid part of Machine Learning framework). / Denna avhandling syftar till undersöka en praktisk ett-steg-i-taget prediktering av strukturmässigt skiftande data, ett icke-stabilt beteende som verkliga data kopplade till människoaktiviteter ofta demonstrerar. Denna uppsättning kan alltså karakteriseras som övervakningsprocess eller monitoringsprocess. Olika prediktionsmodeller, metoder och tillvägagångssätt kan variera från att vara enkla och "beräkningsbilliga" till sofistikerade och "beräkningsdyra". Olika prediktionsmetoder hanterar dessutom olika mönster eller strukturförändringar i data på olika sätt: för vissa typer av data eller vissa dataintervall är vissa prediktionsmetoder bättre än andra, vilket inte brukar vara känt i förväg. Detta väcker en fråga: "Kan man skapa en predictionsprocedur, som effektivt och på ett optimalt sätt skulle byta mellan olika prediktionsmetoder och för att adaptera dess användning till ändringar i inkommande dataflöde?" Avhandlingen svarar på frågan genom att introducera optimalitetskoncept eller optimalitet, något som tillåter ett optimalbyte mellan parallellt utförda prediktionsmetoder, för att på så sätt skräddarsy prediktionsmetoder till förändringar i data. Det visas också, hur ett annat prediktionstillvägagångssätt: kombinationsprediktering, där olika prediktionsmetoder kombineras med hjälp av viktat medelvärde, kan utnyttjas av optimalitetsprincipen och därmed få nytta av den. Alltså, fyra klasser av prediktionsresultat kan betraktas och jämföras: basprediktionsmetoder, basoptimalitet, kombinationsprediktering och kombinationsoptimalitet. Denna avhandling visar, att användning av optimalitet ger resultat, där optimaliteten för det mesta inte är sämre eller bättre än den bästa av enskilda prediktionsmetoder, som själva optimaliteten är baserad på. Optimalitet reducerar också spridningen från mängden av olika prediktionsförslag till ett tal eller bara några enstaka tal (på ett kontrollerat sätt). Optimalitet producerar ytterligare en nedre gräns för optimalprediktion: det hypotetiskt bästa uppnåeliga prediktionsresultatet. Huvudslutsatsen är följande: optimalitetstillvägagångssätt gör att andra traditionella sätt att ta hand om övervakningsprocesser blir mer eller mindre föråldrade: att leta bara efter den enda bästa enskilda prediktionsmetoden för data med strukturskift. Sådan sökning kan fortfarande göras, men det är bäst att göra den inom optimalitetstillvägagångssättet, där den ingår som en naturlig komponent. Allt detta gör det föreslagna optimalitetstillvägagångssättetet för prediktionsändamål till en giltig "representant" för det mer allmäna ensembletillvägagångssättet (något som också motiverade utvecklingen av numera populär Ensembleinlärning som en giltig del av Maskininlärning).